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Fast self-constructing fuzzy neural network-based decision feedback equaliser in time-invariant and time-varying channels

Fast self-constructing fuzzy neural network-based decision feedback equaliser in time-invariant and time-varying channels

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Published

A fast self-constructing fuzzy neural network-based decision feedback equaliser (FSCFNN DFE) is proposed. Without estimating the channel, a fast learning algorithm containing the structure and parameter learning phases is employed to the FSCFNN DFE. Both the partition of the feedforward input space and the gradient descent method are used simultaneously with the aid of decision feedback inputs in this fast learning procedure. The performance of FSCFNN DFE is compared with traditional non-linear equalisers in both time-invariant and time-varying channels. The reduced complexity and excellent performance of the FSCFNN DFE make it suitable for severely distorted channel equalisation.

Inspec keywords: decision feedback equalisers; time-varying channels; recurrent neural nets; fuzzy neural nets

Other keywords: time-invariant channels; time-varying channels; fast learning algorithm; decision feedback equaliser; self-constructing fuzzy neural network

Subjects: Communication channel equalisation and identification; Neural computing techniques

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http://iet.metastore.ingenta.com/content/journals/10.1049/iet-com.2009.0402
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